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http://hdl.handle.net/2445/214355
Title: | Prediction of incident cardiovascular events using machine learning and CMR radiomics |
Author: | Ruiz Pujadas, Esmeralda Raisi-Estabragh, Zahra Szabo, Liliana McCracken, Celeste Izquierdo Morcillo, Cristian Campello Román, Víctor Manuel Martín Isla, Carlos Atehortúa, Angélica Vago, Hajnalka Merkely, Béla Maurovich-Horvath, Pal Harvey, Nicholas C. Neubauer, Stefan Petersen, Steffen E. Lekadir, Karim, 1977- |
Keywords: | Medicina preventiva Fibril·lació auricular Insuficiència cardíaca Aprenentatge automàtic Preventive medicine Atrial fibrillation Heart failure Machine learning |
Issue Date: | 13-Dec-2022 |
Publisher: | Springer Verlag |
Abstract: | Objectives: Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. Methods: We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. Results: AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. Conclusions: Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. |
Note: | Reproducció del document publicat a: https://doi.org/10.1007/s00330-022-09323-z |
It is part of: | European Radiology, 2022, vol. 33, num.5, p. 3488-3500 |
URI: | http://hdl.handle.net/2445/214355 |
Related resource: | https://doi.org/10.1007/s00330-022-09323-z |
ISSN: | 0938-7994 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
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